Conditioning Information, Out-of-Sample Validation, and the Cross-Section of Stock Returns PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Conditioning Information, Out-of-Sample Validation, and the Cross-Section of Stock Returns PDF full book. Access full book title Conditioning Information, Out-of-Sample Validation, and the Cross-Section of Stock Returns by Kevin Q. Wang. Download full books in PDF and EPUB format.
Author: Kevin Q. Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
Get Book Here
Book Description
Empirical research on conditional asset pricing has been built on several standard return-predictive variables. However, recent studies have raised serious doubts on these variables that typically serve as the instruments to capture the relevant conditioning information. In the stochastic discount factor framework, we propose and implement a new approach to assess the value of the standard instruments. We compare the out-of-sample performances of conditional models that are built on different subsets of several widely-used instruments. We find that some combinations of these instruments, after adjusting for the effect of the horse-race over all the subsets, can significantly improve the out-of-sample performance for pricing the cross-section of stock returns. In contrast, some other subsets give rise to conditional models that drastically underperform the unconditional model. The results affirm the value of the conditioning instruments for cross-sectional asset pricing and highlight the importance of instrument selection.
Author: Kevin Q. Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
Get Book Here
Book Description
Empirical research on conditional asset pricing has been built on several standard return-predictive variables. However, recent studies have raised serious doubts on these variables that typically serve as the instruments to capture the relevant conditioning information. In the stochastic discount factor framework, we propose and implement a new approach to assess the value of the standard instruments. We compare the out-of-sample performances of conditional models that are built on different subsets of several widely-used instruments. We find that some combinations of these instruments, after adjusting for the effect of the horse-race over all the subsets, can significantly improve the out-of-sample performance for pricing the cross-section of stock returns. In contrast, some other subsets give rise to conditional models that drastically underperform the unconditional model. The results affirm the value of the conditioning instruments for cross-sectional asset pricing and highlight the importance of instrument selection.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
Get Book Here
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156
Get Book Here
Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Author: Haim Levy
Publisher: Cambridge University Press
ISBN: 1139503022
Category : Business & Economics
Languages : en
Pages : 457
Get Book Here
Book Description
The Capital Asset Pricing Model (CAPM) and the mean-variance (M-V) rule, which are based on classic expected utility theory, have been heavily criticized theoretically and empirically. The advent of behavioral economics, prospect theory and other psychology-minded approaches in finance challenges the rational investor model from which CAPM and M-V derive. Haim Levy argues that the tension between the classic financial models and behavioral economics approaches is more apparent than real. This book aims to relax the tension between the two paradigms. Specifically, Professor Levy shows that although behavioral economics contradicts aspects of expected utility theory, CAPM and M-V are intact in both expected utility theory and cumulative prospect theory frameworks. There is furthermore no evidence to reject CAPM empirically when ex-ante parameters are employed. Professionals may thus comfortably teach and use CAPM and behavioral economics or cumulative prospect theory as coexisting paradigms.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262351307
Category : Business & Economics
Languages : en
Pages : 497
Get Book Here
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: National Bureau of Economic Research
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 476
Get Book Here
Book Description
Author: Radu Burlacu
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Get Book Here
Book Description
We derive a cross-sectional asset pricing measure from a noisy multi-asset rational expectations equilibrium model. The measure is based on the time-series covariance of an asset's returns and security prices. Empirically, stocks with a measure one standard deviation above and below the average have returns that differ by 0.36% the following month (4.44% per annum) which is statistically significant at the 1%-level. Results remain significant after including variables such as stock market capitalization, book-to-market ratio, and the probability of information-based trading. Our measure can be understood as a proxy for information risk and/or supply uncertainty. We show the two explanations are theoretically intertwined.
Author: Constantin Zopounidis
Publisher: Springer Nature
ISBN: 3030666913
Category : Business & Economics
Languages : en
Pages : 480
Get Book Here
Book Description
Risk is the main source of uncertainty for investors, debtholders, corporate managers and other stakeholders. For all these actors, it is vital to focus on identifying and managing risk before making decisions. The success of their businesses depends on the relevance of their decisions and consequently, on their ability to manage and deal with the different types of risk. Accordingly, the main objective of this book is to promote scientific research in the different areas of risk management, aiming at being transversal and dealing with different aspects of risk management related to corporate finance as well as market finance. Thus, this book should provide useful insights for academics as well as professionals to better understand and assess the different types of risk.
Author: John H. Cochrane
Publisher: Now Publishers Inc
ISBN: 1933019158
Category : Business & Economics
Languages : en
Pages : 117
Get Book Here
Book Description
Financial Markets and the Real Economy reviews the current academic literature on the macroeconomics of finance.
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
ISBN: 195292703X
Category : Business & Economics
Languages : en
Pages : 95
Get Book Here
Book Description
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
